Skip to content

CodecCat v0.1.0 — Stable

Choose a tag to compare

View all tags
@github-actions github-actions released this 06 Nov 04:33
· 44 commits to main since this release
v0.1.0
867850d
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

CodecCat v0.1.0 — First Stable Release

CodecCat is a native Python character-encoding detector and strict Unicode decoder. This release formalizes its 0.1.x API compatibility contract and improves distribution portability without retraining the model or changing the proven v0.1.0a5 decoder logic.

Stable behavior and compatibility

  • Supports Python 3.10–3.14 on released Linux, macOS, and Windows targets.
  • Public CodecCat, detect, decode, Detection fields, BOM policy and CLI flags have a documented compatibility contract: docs/STABILITY.md.
  • Invalid decoding is not silently replaced. Encoding inference itself cannot guarantee unique correctness for arbitrary bytes.
  • Native inference has no NumPy/scikit-learn dependencies; legacy API compatibility remains available as an optional extra.

Portability and release engineering

  • Genuine manylinux_2_28 Linux Wheels for x86-64 and ARM64, built and tested in corresponding policy containers.
  • macOS x86-64/ARM64 and Windows x86-64/ARM64 Wheels tested on their own GitHub-hosted runners.
  • Frozen default model SHA256 is identical across six Wheels. Every asset has a checksum in SHA256SUMS and metadata in MODEL_PROVENANCE.json.
  • Wheel build uses verified PEP-517 packaging to include the model files; avoids the previous bug where a direct maturin build omitted its bundled model.

Accuracy evidence and limitations

The decoder model and inference algorithm are unchanged from v0.1.0a5. That version scored 826/843 (97.98%) correctly decoded text on a fixed engineering benchmark, compared with chardet's 829/843 (98.34%). The tested 843 examples include development and training sources, so this is not an independently representative real-world accuracy rate.

Codec identification of sufficiently ambiguous bytes cannot be universally perfect. The inference model was derived from third-party text with mixed upstream rights; rights and provenance remain documented and are not an assertion of legal clearance.

Install: select the Wheel for your OS and CPU from the assets below. On supported Linux/Python versions, the manylinux_2_28 Wheels are preferable to the earlier plain linux_* Alpha files. The release is distributed on GitHub; a PyPI upload is a separate action and not claimed here.

Assets 10
Loading